MoRE: A Mixture of Low-Rank Experts for Adaptive Multi-Task Learning
Dacao Zhang, Kun Zhang, Shimao Chu, Le Wu, Xinqiao Li, Si Wei · 2025
With the rapid development of Large Language Models (LLMs), Parameter-Efficient Fine-Tuning (PEFT) methods have gained significant attention, which aims to achieve efficient fine-tuning of LLMs with fewer parameters.As a representative PEFT method, Low-Rank Adaptation (LoRA) and its variants introduce low-rank matrices to approximate the incremental tuning parameters and achieve impressive performance over multiple scenarios.However, these methods either focus on single-task scenarios or separately train multiple LoRA modules for multi-task scenarios, limiting the efficiency and effectiveness of LoRA in multi-task scenarios.To better adapt to multi-task fine-tuning, in this paper, we propose a novel Mixture of Low-Rank Experts (MoRE) for multi-task PEFT.Specifically, instead of using an individual LoRA for each task, we align different ranks of LoRA module with different tasks, which we named low-rank experts.Moreover, we design a novel adaptive rank selector to select the appropriate expert for each task.By jointly training low-rank experts, MoRE can enhance the adaptability and efficiency of LoRA in multi-task scenarios.Finally, we conduct extensive experiments over multiple multi-task benchmarks along with different LLMs to verify model performance.Experimental results demonstrate that compared to traditional LoRA and its variants, MoRE significantly improves the performance of LLMs in multi-task scenarios and incurs no additional inference cost.We also release the model and code to facilitate the community 1 .